# Website Markdown Crawler (`moorish-dev/website-markdown-crawler`) Actor Crawls a website and converts every page to clean Markdown optimized for LLM ingestion. - **URL**: https://apify.com/moorish-dev/website-markdown-crawler.md - **Developed by:** [Ziad Tarik](https://apify.com/moorish-dev) (community) - **Categories:** SEO tools, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 website analyzeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Website Markdown Crawler Crawls a website starting from a seed URL and converts every page to clean Markdown optimized for LLM ingestion (LlamaIndex, LangChain, OpenAI, Pinecone). Output includes structured metadata per page: title, language detected, publication date, headings outline, word count, and chunked content ready for vector store upsert. ### Features - **Clean Markdown Extraction:** Strips noise (navigation, footers) to extract just the main content. - **Smart Chunking:** Splits content into token chunks respecting paragraph boundaries. - **Language Filtering:** Can automatically detect and filter pages by language (e.g., only `en` or `fr`). - **Domain Control:** Keeps the crawler scoped to the seed URL's domain. - **Regex Exclusions:** Skip non-valuable URLs like tags or author pages. ### Output Example Each crawled page yields a structured JSON record: ```json { "url": "https://docs.example.com/getting-started", "title": "Getting Started — Example Docs", "description": "Learn how to set up Example in 5 minutes.", "language": "en", "wordCount": 842, "tokenEstimate": 1120, "headings": [ { "level": 1, "text": "Getting Started" }, { "level": 2, "text": "Installation" } ], "markdown": "# Getting Started\n\nLearn how to...", "chunks": [ { "index": 0, "content": "# Getting Started\n\nLearn how to...", "tokenEstimate": 498 } ], "chunkCount": 1, "depth": 1, "crawledAt": "2026-06-10T14:32:00.000Z" } ```` ### Integrations Connect the crawler directly into your RAG stack. #### LlamaIndex ```python from llama_index.core import Document ## After running the Actor, download dataset as JSON docs = [ Document(text=chunk['content'], metadata={'url': item['url'], 'chunk': chunk['index']}) for item in dataset_items for chunk in item['chunks'] ] ``` #### LangChain ```python from langchain.docstore.document import Document as LCDoc lc_docs = [ LCDoc(page_content=chunk['content'], metadata={'source': item['url']}) for item in dataset_items for chunk in item['chunks'] ] ``` # Actor input Schema ## `startUrls` (type: `array`): One or more URLs to start crawling from. The crawler stays within each URL's domain. ## `maxPages` (type: `integer`): Maximum number of pages to crawl per start URL ## `crawlDepth` (type: `integer`): How many link hops from the start URL. 1 = start URL only. 2 = start + linked pages. ## `stayOnDomain` (type: `boolean`): Do not follow links to external domains ## `includeChunks` (type: `boolean`): Split Markdown into chunks of ~500 tokens for direct vector store upsert ## `chunkSize` (type: `integer`): Approximate token count per chunk (1 token ≈ 4 characters) ## `excludeUrlPatterns` (type: `array`): Regex patterns. URLs matching any pattern are skipped. ## `contentSelectors` (type: `array`): CSS selectors for main content area. Helps strip navbars/footers. Leave empty for automatic detection. ## `outputLanguageFilter` (type: `array`): Only keep pages in these languages (e.g., en, fr, es). Empty = keep all. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.example.com" } ], "maxPages": 50, "crawlDepth": 3, "stayOnDomain": true, "includeChunks": true, "chunkSize": 500, "excludeUrlPatterns": [ "/tag/", "/author/", "\\?", "#" ], "contentSelectors": [ "article", "main", "div.content", ".post-body" ], "outputLanguageFilter": [] } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://docs.example.com" } ], "excludeUrlPatterns": [ "/tag/", "/author/", "\\?" ] }; // Run the Actor and wait for it to finish const run = await client.actor("moorish-dev/website-markdown-crawler").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [{ "url": "https://docs.example.com" }], "excludeUrlPatterns": [ "/tag/", "/author/", "\\?", ], } # Run the Actor and wait for it to finish run = client.actor("moorish-dev/website-markdown-crawler").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://docs.example.com" } ], "excludeUrlPatterns": [ "/tag/", "/author/", "\\\\?" ] }' | apify call moorish-dev/website-markdown-crawler --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=moorish-dev/website-markdown-crawler", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Website Markdown Crawler", "description": "Crawls a website and converts every page to clean Markdown optimized for LLM ingestion.", "version": "1.0", "x-build-id": "yMevAgDK8ZKt47V1W" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/moorish-dev~website-markdown-crawler/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-moorish-dev-website-markdown-crawler", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/moorish-dev~website-markdown-crawler/runs": { "post": { "operationId": "runs-sync-moorish-dev-website-markdown-crawler", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/moorish-dev~website-markdown-crawler/run-sync": { "post": { "operationId": "run-sync-moorish-dev-website-markdown-crawler", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "One or more URLs to start crawling from. The crawler stays within each URL's domain.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPages": { "title": "Max pages to crawl", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of pages to crawl per start URL", "default": 50 }, "crawlDepth": { "title": "Crawl depth", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many link hops from the start URL. 1 = start URL only. 2 = start + linked pages.", "default": 3 }, "stayOnDomain": { "title": "Stay on same domain", "type": "boolean", "description": "Do not follow links to external domains", "default": true }, "includeChunks": { "title": "Include LLM chunks", "type": "boolean", "description": "Split Markdown into chunks of ~500 tokens for direct vector store upsert", "default": true }, "chunkSize": { "title": "Chunk size (tokens)", "minimum": 100, "maximum": 2000, "type": "integer", "description": "Approximate token count per chunk (1 token ≈ 4 characters)", "default": 500 }, "excludeUrlPatterns": { "title": "URL patterns to exclude", "type": "array", "description": "Regex patterns. URLs matching any pattern are skipped.", "items": { "type": "string" } }, "contentSelectors": { "title": "Main content CSS selectors (optional)", "type": "array", "description": "CSS selectors for main content area. Helps strip navbars/footers. Leave empty for automatic detection.", "items": { "type": "string" } }, "outputLanguageFilter": { "title": "Filter by language", "type": "array", "description": "Only keep pages in these languages (e.g., en, fr, es). Empty = keep all.", "default": [], "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```